Intelligence statistical process control in cellular manufacturing based on SVM

Authors:
Shaoxiong Wu
Affiliations:
Dept. of Economics and Management, Fujian University of Technology, Fuzhou, Fujian, P.R. China
Venue:
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Year:
2011

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Abstract

According to peculiarity of cellular manufacturing, the method of drawing control chart was proposal. In the modeling of structure for patterns recognition of control chart in cellular manufacturing, the mixture kernel function was proposed, and one-against-one algorithm multi-class classification support vector machine was applied, and genetic algorithm was used to optimize the parameters of SVM. The simulation results show that the performance of mixture kernel is superior to a single common kernel, and it can recognize each pattern of the control chart accurately, and it is superior to probabilistic neural network and wavelet probabilistic neural network in the aggregate classification rate, type I error, type II error, and also has such advantages as simple structure, quick convergence, which can be used in control chart patterns recognition in cellular manufacturing.